基于改进的YOLOv10的零售业增强自检系统
Lianghao Tan1, Shubing Liu2, Jing Gao3
1Department of Information System, Arizona State University, Tempe, AZ 85281, USA.
Journal of imaging
|October 25, 2024
概括
本研究介绍了改进的YOLOv10深度学习模型,用于零售自动结账系统. 改进后的系统提高了产品识别准确度和结账速度,降低了零售自动化中的劳动力成本.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 零售自动化 零售自动化
背景情况:
- 深度学习和计算机视觉为自动化零售业务提供了巨大的潜力.
- 当前的自动结账系统在效率和准确性方面面临挑战.
- 劳动力成本是零售业的一个主要问题.
研究的目的:
- 为零售环境开发一个先进的自检系统.
- 为了提高产品识别准确性和结账速度.
- 通过增强自动化来降低运营成本.
主要方法:
- 改进的YOLOv10网络被用于产品检测.
- 通过YOLOv8检测头结构优化了YOLOv10模型.
- 为自行结账场景开发了一种专门的后处理算法.
主要成果:
- 与现有方法相比,拟议的系统显示出更高的产品识别准确性.
- 实验结果证实了提升的结账速度.
- 优化的模型和算法显著提高了系统性能.
结论:
- 新型自动结账系统为零售自动化提供了可行的技术解决方案.
- 优化的深度学习模型可以有效地应用于现实世界的零售挑战.
- 这项研究有助于提高结账效率,降低劳动力成本.
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